Product managers have historically had limited ways to measure how customers interact with configurable products during the sales process. An RFQ can reveal initial customer requirements, but it doesn’t tell the story of how configuration pathways influence the buying process up to the quote. 

If you want to understand customer intent, which configurations contribute to a longer cycle time, or where your initiatives to standardize configurations are working, then you need to access data that has typically been reserved for CPQ owners and sales teams. 

What makes configuration data so valuable for product managers? 

Configuration intelligence is the ability to visualize the full process of the quoting cycle, from initial configuration to the fully evolved solution or quote. CPQ captures customer intent data, rather than just completed transactions, so you can see how a configuration changed over time, or where it stalled or reversed course.  

Product managers gain unique insights from CPQ data, including configurations that were selected but never reached a final quote as well as recurring custom requests or modified product options that require heavy approvals. 

Each of these insights supports the questions you ask on a daily basis, like  

• Which product configurations align most closely with real customer applications? 

• Where is product complexity creating friction for buyers, sales, or engineering?  

• Which recurring “custom” requests should become standardized or modular?  

• Where are sales teams deviating from standard configurations? 

• Which configuration options are consistently linked to longer cycle times? 

• Which product combinations are gaining traction in specific markets or segments? 

 

CPQ data has mostly been used to improve quoting workflows or CPQ models. They’ve been highly underutilized as a strategic tool for product managers. To get to the nuts of bolts of these questions, embedded CPQ analytics is the ideal emerging option for product teams to find direct answers. 

Using embedded, contextualized CPQ analytics solves key pain points for your team. 

Using CPQ data to optimize ETO-to-CTO transitions 

Standardization efforts are usually judged after the fact, by counting custom orders against standard ones or tallying engineering approvals. That tells you the size of the problem, not where it’s coming from. 

Configuration data can tell you which “one-off” requests aren’t actually one-off. For example, you may find that 99% of what looked like unique engineer-to-order requests were the same request recurring, a strong signal that the work belonged in the configurator, rather than an engineer’s desk. Once a recurring pattern is visible, the question changes from “how do we approve this faster” to “what’s the smallest set of guided questions that gets a customer to this outcome without engineering involved at all. 

How to use configuration data to support your modular product strategy 

You need to easily see the configuration process from start to finish and then see that data across all configurations.  

Recurring “custom” requests may indicate opportunities for standardization, missing modules, or evolving market demand. Yet these custom requests may not show original intent in your ERP system. Or, you may not see all of the requests that were not approved during the configuration cycle.  

With configuration data, you can also look at where a standardized component is consistently paired with a nonstandard one that needs approval, a pattern worth investigating even before it shows up across the full portfolio. Is there an opportunity to standardize further, or adjust a standard component to be more useful? 

You can also weigh how often a custom request comes up against how much friction it adds along the way, rather than looking at order counts alone. That’s where modularization or approval steps are causing delays worth fixing. 

Seeing all of the information before the order allows you to identify missing modules and see evolving market needs that doesn’t show up in the final Bill of Materials. In doing so, you can update your portfolio to create more operational scalability and sales efficiency.  

What can configuration approval data reveal about product complexity? 

Today, 55% of manufacturers manage configuration complexity through a mix of guided quoting and downstream engineering adjustments. 

Many teams currently use CPQ data to improve their CPQ configuration models, but this is usually more operational.  

What are the strategic benefits for product and engineering teams in using configuration data to improve these CPQ models and the configuration pathways for buyers and sellers?  

Take, for example, repeated approvals. Frequent engineering approvals for a specific product component or option may reveal opportunities to simplify the configuration model or standardize recurring requests to streamline customers’ buying experience. By analyzing what users select, which options are consistently overridden or approved, and which BOM components are actually included in final products, manufacturers can identify unnecessary complexity in the configuration process.  

For example, if certain guided configuration questions rarely influence the final outcome, they may be creating confusion. If sales requests repeated approvals, this could influence configuration pathways. Certain guided questions may rarely help in conversion. Knowing this creates opportunities to streamline the buying and configuration experience for both sales teams and customers. Product managers can identify what helps convert and what creates operational overhead.   

How can CPQ data help improve product portfolio decisions? 

Configuration data provides a unique way to measure whether your product portfolio is evolving toward a more scalable configure-to-order model. By analyzing recurring custom requests, frequently selected option combinations, and configuration-to-quote conversions, product managers can identify which customer requirements are becoming common enough to standardize. 

That said, configuration data on its own doesn’t tell the whole story. A configuration that stalls or doesn’t convert isn’t automatically a lost cause; it may have supported a different deal, tested a market before it was ready, or served a strategic account for reasons that never show up in a quote. The strongest portfolio decisions come from pairing configuration behavior with the context that only sales, engineering, and account teams have: why a path was taken, and what it was really for. 

Used this way, configuration data is an important place to look to complete the full picture. 

Configuration-aware intelligence is the new commercial imperative for product and engineering teams 

Manufacturers already collect enormous amounts of configuration and quoting data, but most organizations still use it primarily to support quoting operations. Product managers who operationalize CPQ intelligence can reduce portfolio complexity, identify new standardization opportunities, optimize modular product strategies, and align product investments more closely with how customers buy. 

Configuration-aware intelligence also creates a shared view across product management, engineering, sales, and operations. Instead of relying solely on historical orders, engineering feedback, or disconnected reporting systems, teams can make portfolio decisions based on real customer configuration behavior. 

As manufacturers continue to balance engineer-to-order and configure-to-order business models, configuration data is becoming an increasingly important input into product strategy. 

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About the Author

Helena Aurell is a Product Manager at Tacton, where she brings a rare vantage point spanning product, delivery, and the customer side of the business. Having worked with Tacton’s customers across both North America and Sweden, from implementation projects to roadmap and service strategy, her work includes bringing configuration-aware intelligence and analytics to Tacton customers.